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David J. Burinsky – One of the best experts on this subject based on the ideXlab platform.
Hybrid mass Spectrometers for tandem mass spectrometryJournal of the American Society for Mass Spectrometry, 2008Co-Authors: Gary L Glish, David J. BurinskyAbstract:
Mass Spectrometers that use different types of analyzers for the first and second stages of mass analysis in tandem mass spectrometry (MS/MS) experiments are often referred to as “hybrid” mass Spectrometers. The general goal in the design of a hybrid instrument is to combine different performance characteristics offered by various types of analyzers into one mass Spectrometer. These performance characteristics may include mass resolving power, the ion kinetic energy for collision-induced dissdissociation, and speed of analysis. This paper provides a review of the development of hybrid instruments over the last 30 years for analytical applications.
Denis J Phares – One of the best experts on this subject based on the ideXlab platform.
a comparison of particle mass Spectrometers during the 1999 atlanta supersite projectJournal of Geophysical Research, 2003Co-Authors: Ann M. Middlebrook, Denis J Phares, D M Murphy, D S Thomson, Kimberly A. Prather, Ryan J WenzelAbstract:
During the Atlanta Supersite Project, four particle mass Spectrometers were operated together for the first time: NOAA’s Particle Analysis by Laser Mass Spectrometer (PALMS), University of California at Riverside’s Aerosol Time-of-Flight Mass Spectrometer (ATOFMS), University of Delaware’s Rapid Single-Particle Mass Spectrometer II (RSMS-II), and Aerodyne’s Aerosol Mass Spectrometer (AMS). Although these mass Spectrometers are generally classified as similar instruments, they clearly have different characteristics due to their unique designs. One primary difference is related to the volatilization/ionization method: PALMS, ATOFMS, and RSMS-II utilize laser desorption/ionization, whereas particles in the AMS instrument are volatilized by impaction onto a heated surface with the resulting components ionized by electron impact. Thus mass spectral data from the AMS are representative of the ensemble of particles sampled, and those from the laser-based instruments are representative of individual particles. In addition, the AMS instrument cannot analyze refractory material such as soot, sodium chloride, and crustal elements, and some sulfate or water-rich particles may not always be analyzed with every laser-based instrument. A main difference among the laser-based mass Spectrometers is that the RSMS-II instrument can obtain size-resolved single particle composition information for particles with aerodynamic diameters as small as 15 nm. The minimum sizes analyzed by ATOFMS and PALMS are 0.2 and about 0.35 μm, respectively, in aerodynamic diameter. Furthermore, PALMS, ATOFMS, and RSMS-II use different laser ionization conditions. Despite these differences the laser-based instruments found similar individual particle classifications, and their relative fractions among comparable sized particles from Atlanta were broadly consistent. Finally, the AMS measurements of the nitrate/sulfate mole ratio were highly correlated with composite measurements (r^2 = 0.93). In contrast, the PALMS nitrate/sulfate ion ratios were only moderately correlated (r^2 ∼ 0.7).
Antonio Plaza – One of the best experts on this subject based on the ideXlab platform.
Spectrometer-Driven Spectral Partitioning for Hyperspectral Image ClassificationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016Co-Authors: Jun Li, Antonio PlazaAbstract:
Classification is an important and widely used technique for remotely sensed hyperspectral data interpretation. Although most techniques developed for hyperspectral imagimage classification assume that the spectral signatures provided by an imaging Spectrometer can be interpreted as a unique and continuous signal, in practice, this signal may be obtained after the combination of several individual responses obtained from different Spectrometers. In this work, we propose a new spectral partitioning strategy prior to classification which takes into account the physical design of the imaging Spectrometer system for partitioning the spectral bands collected by each Spectrometer, and resampling them into different groups or partitions. The final classification result is obtained as a combination of the results obtained from each individual partition by means of a multiple classifier system (MCS). The proposed strategy not only incorporates the design of the imaging Spectrometer into the classification process but also circumvents problems such as the curse of dimensionality given by the unbalance between the high number of spectral bands and the generally limited number of training samples available for classification purposes. This concept is illustrated in this work using two different imaging Spectrometers: the airborne visible infra-red imaging Spectrometer, operated by NASA, and the digital airborne imaging system (DAIS), operated by the German Aerospace Center. Our experiments indicate that the proposed spectral partitioning strategy can lead to classification improvements on the order of 5% overall accuracy when using state-of-the-art spatial-spectral classifiers with very limited training samples.
Spectrometer-driven spectral partitioning for hyperspectral image classification2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014Co-Authors: Jun Li, Antonio Plaza, Peijun Du, Mahdi KhodadadzadehAbstract:
Classification is an important and widely used technique for remotely sensed hyperspectral data interpretation. Although most techniques developed for classification assume that the spectral signatures provided by an imaging Spectrometer can be interpreted as a unique and continuous signal, in practice this signal may be obtained after the combined individual responses from several different Spectrometers. For instance, the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system is in fact formed by four different Spectrometers, covering the nominal spectral ranges of 400-700 nm, 700-1300 nm, 1300-1900 nm, and 1900-2500 nm, respectively. In this work, we propose a new classification strategy which takes into account the physical design of the imaging Spectrometer system for partitioning the spectral bands collected by each Spectrometer, and resampling them into different groups or partitions. The final classification result is obtained as a combination of the results obtained from each individual partition. This concept is illustrated in this work using AVIRIS data, and our experimental results indicate that the proposed strategy provides advantages in terms of classification accuracy, in particular, when very limited training samples are available.